mirror of
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Update Primus Megatron doc v25.8 (#5396)
* megatron: update previous versions list update wording * megatron: update rst and yaml update primus repo link update mig guide * update headings and anchors * megatron: update doc * update docker hub urls
This commit is contained in:
Peter Park
@@ -127,7 +127,9 @@ article_pages = [
|
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.6", "os": ["linux"]},
|
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.7", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v25.7", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-megatron", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
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{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history", "os": ["linux"]},
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@@ -1,13 +1,12 @@
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dockers:
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- pull_tag: rocm/megatron-lm:v25.7_py310
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docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
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- pull_tag: rocm/megatron-lm:v25.8_py310
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docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.8_py310/images/sha256-50fc824361054e445e86d5d88d5f58817f61f8ec83ad4a7e43ea38bbc4a142c0
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components:
|
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ROCm: 6.4.2
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Primus: v0.1.0-rc1
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ROCm: 6.4.3
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PyTorch: 2.8.0a0+gitd06a406
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Python: "3.10"
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Transformer Engine: 2.1.0.dev0+ba586519
|
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hipBLASLt: 37ba1d36
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Transformer Engine: 2.2.0.dev0+54dd2bdc
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hipBLASLt: d1b517fc7a
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Triton: 3.3.0
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RCCL: 2.22.3
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model_groups:
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@@ -0,0 +1,49 @@
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dockers:
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- pull_tag: rocm/megatron-lm:v25.7_py310
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docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
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components:
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ROCm: 6.4.2
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Primus: v0.1.0-rc1
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PyTorch: 2.8.0a0+gitd06a406
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Python: "3.10"
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Transformer Engine: 2.1.0.dev0+ba586519
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hipBLASLt: 37ba1d36
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Triton: 3.3.0
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RCCL: 2.22.3
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model_groups:
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- group: Meta Llama
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tag: llama
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models:
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- model: Llama 3.3 70B
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mad_tag: pyt_megatron_lm_train_llama-3.3-70b
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- model: Llama 3.1 8B
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mad_tag: pyt_megatron_lm_train_llama-3.1-8b
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- model: Llama 3.1 70B
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mad_tag: pyt_megatron_lm_train_llama-3.1-70b
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- model: Llama 3.1 70B (proxy)
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mad_tag: pyt_megatron_lm_train_llama-3.1-70b-proxy
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- model: Llama 2 7B
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mad_tag: pyt_megatron_lm_train_llama-2-7b
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- model: Llama 2 70B
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mad_tag: pyt_megatron_lm_train_llama-2-70b
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- group: DeepSeek
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tag: deepseek
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models:
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- model: DeepSeek-V3 (proxy)
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mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
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- model: DeepSeek-V2-Lite
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mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
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- group: Mistral AI
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tag: mistral
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models:
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- model: Mixtral 8x7B
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mad_tag: pyt_megatron_lm_train_mixtral-8x7b
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- model: Mixtral 8x22B (proxy)
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mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
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- group: Qwen
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tag: qwen
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models:
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- model: Qwen 2.5 7B
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mad_tag: pyt_megatron_lm_train_qwen2.5-7b
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- model: Qwen 2.5 72B
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mad_tag: pyt_megatron_lm_train_qwen2.5-72b
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@@ -0,0 +1,58 @@
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dockers:
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- pull_tag: rocm/megatron-lm:v25.7_py310
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docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
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components:
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ROCm: 6.4.2
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Primus: v0.1.0-rc1
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PyTorch: 2.8.0a0+gitd06a406
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Python: "3.10"
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Transformer Engine: 2.1.0.dev0+ba586519
|
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hipBLASLt: 37ba1d36
|
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Triton: 3.3.0
|
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RCCL: 2.22.3
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model_groups:
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- group: Meta Llama
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tag: llama
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models:
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- model: Llama 3.3 70B
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mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
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config_name: llama3.3_70B-pretrain.yaml
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- model: Llama 3.1 70B
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mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
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config_name: llama3.1_70B-pretrain.yaml
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- model: Llama 3.1 8B
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mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
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config_name: llama3.1_8B-pretrain.yaml
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- model: Llama 2 7B
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mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
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config_name: llama2_7B-pretrain.yaml
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- model: Llama 2 70B
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mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
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config_name: llama2_70B-pretrain.yaml
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- group: DeepSeek
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tag: deepseek
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models:
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- model: DeepSeek-V3 (proxy)
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mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
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config_name: deepseek_v3-pretrain.yaml
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- model: DeepSeek-V2-Lite
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mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
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config_name: deepseek_v2_lite-pretrain.yaml
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- group: Mistral AI
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tag: mistral
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models:
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- model: Mixtral 8x7B
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mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
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config_name: mixtral_8x7B_v0.1-pretrain.yaml
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- model: Mixtral 8x22B (proxy)
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mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
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config_name: mixtral_8x22B_v0.1-pretrain.yaml
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- group: Qwen
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tag: qwen
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models:
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- model: Qwen 2.5 7B
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mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
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config_name: primus_qwen2.5_7B-pretrain.yaml
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- model: Qwen 2.5 72B
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mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
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config_name: qwen2.5_72B-pretrain.yaml
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@@ -1,13 +1,13 @@
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dockers:
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- pull_tag: rocm/megatron-lm:v25.7_py310
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docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
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- pull_tag: rocm/megatron-lm:v25.8_py310
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docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.8_py310/images/sha256-50fc824361054e445e86d5d88d5f58817f61f8ec83ad4a7e43ea38bbc4a142c0
|
||||
components:
|
||||
ROCm: 6.4.2
|
||||
Primus: v0.1.0-rc1
|
||||
ROCm: 6.4.3
|
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Primus: 927a717
|
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PyTorch: 2.8.0a0+gitd06a406
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Python: "3.10"
|
||||
Transformer Engine: 2.1.0.dev0+ba586519
|
||||
hipBLASLt: 37ba1d36
|
||||
Transformer Engine: 2.2.0.dev0+54dd2bdc
|
||||
hipBLASLt: d1b517fc7a
|
||||
Triton: 3.3.0
|
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RCCL: 2.22.3
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model_groups:
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@@ -16,12 +16,22 @@ previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https
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- Components
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- Resources
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* - v25.7 (latest)
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* - v25.8 (latest)
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-
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* ROCm
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* PyTorch
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* ROCm 6.4.3
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* PyTorch 2.8.0a0+gitd06a406
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-
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* :doc:`Documentation <../megatron-lm>`
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* :doc:`Primus Megatron documentation <../primus-megatron>`
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* :doc:`Megatron-LM (legacy) documentation <../megatron-lm>`
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* `Docker Hub (py310) <https://hub.docker.com/r/rocm/megatron-lm/tags>`__
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* - v25.7
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-
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* ROCm 6.4.2
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* PyTorch 2.8.0a0+gitd06a406
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-
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* :doc:`Primus Megatron documentation <primus-megatron-v25.7>`
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* :doc:`Megatron-LM (legacy) documentation <megatron-lm-v25.7>`
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* `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a>`__
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* - v25.6
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@@ -1,12 +1,12 @@
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:orphan:
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**********************************************************************
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Migrating workloads to Primus (Megatron-Core backend) from Megatron-LM
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**********************************************************************
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*****************************************************************
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Migrating workloads to Primus (Megatron backend) from Megatron-LM
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*****************************************************************
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Primus supports Megatron-Core as backend optimization library,
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replacing ROCm Megatron-LM. This document outlines the steps to migrate
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workload from ROCm Megatron-LM to Primus with the Megatron-Core backend.
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workload from ROCm Megatron-LM to Primus with the Megatron backend.
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Model architecture
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==================
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,604 @@
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:orphan:
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|
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.. meta::
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:description: How to train a model using Megatron-LM for ROCm.
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:keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
|
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|
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********************************************
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Training a model with Primus and Megatron-LM
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********************************************
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.. caution::
|
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This documentation does not reflect the latest version of ROCm Megatron-LM
|
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training performance documentation. See :doc:`../primus-megatron` for the latest version.
|
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|
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`Primus <https://github.com/AMD-AGI/Primus>`__ is a unified and flexible
|
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LLM training framework designed to streamline training. It streamlines LLM
|
||||
training on AMD Instinct accelerators using a modular, reproducible configuration paradigm.
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Primus is backend-agnostic and supports multiple training engines -- including Megatron.
|
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|
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.. note::
|
||||
|
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Primus with the Megatron backend is intended to replace ROCm
|
||||
Megatron-LM in this Dockerized training environment. To learn how to migrate
|
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workloads from Megatron-LM to Primus with Megatron, see
|
||||
:doc:`megatron-lm-primus-migration-guide`.
|
||||
|
||||
For ease of use, AMD provides a ready-to-use Docker image for MI300 series accelerators
|
||||
containing essential components for Primus and Megatron-LM.
|
||||
|
||||
.. note::
|
||||
|
||||
This Docker environment is based on Python 3.10 and Ubuntu 22.04. For an alternative environment with
|
||||
Python 3.12 and Ubuntu 24.04, see the :doc:`previous ROCm Megatron-LM v25.6 Docker release <megatron-lm-v25.6>`.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.7-benchmark-models.yaml
|
||||
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||||
{% set dockers = data.dockers %}
|
||||
{% set docker = dockers[0] %}
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Software component
|
||||
- Version
|
||||
|
||||
{% for component_name, component_version in docker.components.items() %}
|
||||
* - {{ component_name }}
|
||||
- {{ component_version }}
|
||||
{% endfor %}
|
||||
|
||||
.. _amd-primus-megatron-lm-model-support-v257:
|
||||
|
||||
Supported models
|
||||
================
|
||||
|
||||
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
|
||||
Some instructions, commands, and training examples in this documentation might
|
||||
vary by model -- select one to get started.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set model_groups = data.model_groups %}
|
||||
.. raw:: html
|
||||
|
||||
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
|
||||
<div class="row gx-0">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Model</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row gx-0 pt-1">
|
||||
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
|
||||
<div class="row col-10 pe-0">
|
||||
{% for model_group in model_groups %}
|
||||
{% set models = model_group.models %}
|
||||
{% for model in models %}
|
||||
{% if models|length % 3 == 0 %}
|
||||
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% else %}
|
||||
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
.. note::
|
||||
|
||||
Some models, such as Llama, require an external license agreement through
|
||||
a third party (for example, Meta).
|
||||
|
||||
System validation
|
||||
=================
|
||||
|
||||
Before running AI workloads, it's important to validate that your AMD hardware is configured
|
||||
correctly and performing optimally.
|
||||
|
||||
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
|
||||
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
|
||||
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
|
||||
before starting training.
|
||||
|
||||
To test for optimal performance, consult the recommended :ref:`System health benchmarks
|
||||
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
|
||||
system's configuration.
|
||||
|
||||
.. _mi300x-amd-primus-megatron-lm-training-v257:
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set dockers = data.dockers %}
|
||||
{% set docker = dockers[0] %}
|
||||
|
||||
Environment setup
|
||||
=================
|
||||
|
||||
Use the following instructions to set up the environment, configure the script to train models, and
|
||||
reproduce the benchmark results on MI300X series accelerators with the ``{{ docker.pull_tag }}`` image.
|
||||
|
||||
.. _amd-primus-megatron-lm-requirements-v257:
|
||||
|
||||
Download the Docker image
|
||||
-------------------------
|
||||
|
||||
1. Use the following command to pull the Docker image from Docker Hub.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker pull {{ docker.pull_tag }}
|
||||
|
||||
2. Launch the Docker container.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker run -it \
|
||||
--device /dev/dri \
|
||||
--device /dev/kfd \
|
||||
--device /dev/infiniband \
|
||||
--network host --ipc host \
|
||||
--group-add video \
|
||||
--cap-add SYS_PTRACE \
|
||||
--security-opt seccomp=unconfined \
|
||||
--privileged \
|
||||
-v $HOME:$HOME \
|
||||
--shm-size 128G \
|
||||
--name primus_training_env \
|
||||
{{ docker.pull_tag }}
|
||||
|
||||
3. Use these commands if you exit the ``primus_training_env`` container and need to return to it.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
docker start primus_training_env
|
||||
docker exec -it primus_training_env bash
|
||||
|
||||
The Docker container hosts verified release tag ``v0.1.0-rc1`` of the `Primus
|
||||
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1>`__ repository.
|
||||
|
||||
.. _amd-primus-megatron-lm-environment-setup-v257:
|
||||
|
||||
Configuration
|
||||
=============
|
||||
|
||||
Primus defines a training configuration in YAML for each model in
|
||||
`examples/megatron/configs <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/megatron/configs>`__.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set model_groups = data.model_groups %}
|
||||
{% for model_group in model_groups %}
|
||||
{% for model in model_group.models %}
|
||||
.. container:: model-doc {{ model.mad_tag }}
|
||||
|
||||
To update training parameters for {{ model.model }}, you can update ``examples/megatron/configs/{{ model.config_name }}``.
|
||||
Note that training configuration YAML files for other models follow this naming convention.
|
||||
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
|
||||
.. note::
|
||||
|
||||
See :ref:`Key options <amd-primus-megatron-lm-benchmark-test-vars>` for more information on configuration options.
|
||||
|
||||
Dataset options
|
||||
---------------
|
||||
|
||||
You can use either mock data or real data for training.
|
||||
|
||||
* Mock data can be useful for testing and validation. Use the ``mock_data`` field to toggle between mock and real data. The default
|
||||
value is ``true`` for enabled.
|
||||
|
||||
.. code-block:: yaml
|
||||
|
||||
mock_data: true
|
||||
|
||||
* If you're using a real dataset, update the ``train_data_path`` field to point to the location of your dataset.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
mock_data: false
|
||||
train_data_path: /path/to/your/dataset
|
||||
|
||||
Ensure that the files are accessible inside the Docker container.
|
||||
|
||||
.. _amd-primus-megatron-lm-tokenizer-v257:
|
||||
|
||||
Tokenizer
|
||||
---------
|
||||
|
||||
In Primus, each model uses a tokenizer from Hugging Face. For example, Llama
|
||||
3.1 8B model uses ``tokenizer_model: meta-llama/Llama-3.1-8B`` and
|
||||
``tokenizer_type: Llama3Tokenizer`` defined in the `llama3.1-8B model
|
||||
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/primus/configs/models/megatron/llama3.1_8B.yaml>`__
|
||||
definition. As such, you need to set the ``HF_TOKEN`` environment variable with
|
||||
right permissions to access the tokenizer for each model.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
# Export your HF_TOKEN in the workspace
|
||||
export HF_TOKEN=<your_hftoken>
|
||||
|
||||
.. _amd-primus-megatron-lm-run-training-v257:
|
||||
|
||||
Run training
|
||||
============
|
||||
|
||||
Use the following example commands to set up the environment, configure
|
||||
:ref:`key options <amd-primus-megatron-lm-benchmark-test-vars>`, and run training on
|
||||
MI300X series accelerators with the AMD Megatron-LM environment.
|
||||
|
||||
Single node training
|
||||
--------------------
|
||||
|
||||
To run training on a single node, navigate to ``/workspace/Primus`` and use the following setup command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
pip install -r requirements.txt
|
||||
export HSA_NO_SCRATCH_RECLAIM=1
|
||||
export NVTE_CK_USES_BWD_V3=1
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
|
||||
|
||||
To run pre-training for Llama 3.3 70B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh \
|
||||
--micro_batch_size 2 \
|
||||
--global_batch_size 16 \
|
||||
--train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
|
||||
|
||||
To run pre-training for Llama 3.1 8B FP8, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh \
|
||||
--train_iters 50 \
|
||||
--fp8 hybrid
|
||||
|
||||
For Llama 3.1 8B BF16, use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh --train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
|
||||
|
||||
To run pre-training for Llama 3.1 70B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh \
|
||||
--train_iters 50
|
||||
|
||||
To run the training on a single node for Llama 3.1 70B FP8 with proxy, use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh \
|
||||
--train_iters 50 \
|
||||
--num_layers 40 \
|
||||
--fp8 hybrid \
|
||||
--no_fp8_weight_transpose_cache true
|
||||
|
||||
.. note::
|
||||
|
||||
Use two or more nodes to run the *full* Llama 70B model with FP8 precision.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
|
||||
|
||||
To run pre-training for Llama 2 7B FP8, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh \
|
||||
--train_iters 50 \
|
||||
--fp8 hybrid
|
||||
|
||||
To run pre-training for Llama 2 7B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh --train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
|
||||
|
||||
To run pre-training for Llama 2 70B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
|
||||
bash ./examples/run_pretrain.sh --train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v3-proxy
|
||||
|
||||
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
|
||||
use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/deepseek_v3-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh \
|
||||
--num_layers 3 \
|
||||
--moe_layer_freq 1 \
|
||||
--train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
|
||||
|
||||
To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel),
|
||||
use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/deepseek_v2_lite-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh \
|
||||
--global_batch_size 256 \
|
||||
--train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
|
||||
|
||||
To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
|
||||
use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh --train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
|
||||
|
||||
To run training on a single node for Mixtral 8x7B (MoE with expert parallel) with 4-layer proxy,
|
||||
use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/mixtral_8x22B_v0.1-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh \
|
||||
--num_layers 4 \
|
||||
--pipeline_model_parallel_size 1 \
|
||||
--micro_batch_size 1 \
|
||||
--global_batch_size 16 \
|
||||
--train_iters 50
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-7b
|
||||
|
||||
To run training on a single node for Qwen 2.5 7B BF16, use the following
|
||||
command:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh --train_iters 50
|
||||
|
||||
For FP8, use the following command.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh \
|
||||
--train_iters 50 \
|
||||
--fp8 hybrid
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
|
||||
|
||||
To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
|
||||
bash examples/run_pretrain.sh --train_iters 50
|
||||
|
||||
Multi-node training examples
|
||||
----------------------------
|
||||
|
||||
To run training on multiple nodes, you can use the
|
||||
`run_slurm_pretrain.sh <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/run_slurm_pretrain.sh>`__
|
||||
to launch the multi-node workload. Use the following steps to setup your environment:
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.7-benchmark-models.yaml
|
||||
|
||||
{% set dockers = data.dockers %}
|
||||
{% set docker = dockers[0] %}
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
cd /workspace/Primus/
|
||||
export DOCKER_IMAGE={{ docker.pull_tag }}
|
||||
export HF_TOKEN=<your_HF_token>
|
||||
export HSA_NO_SCRATCH_RECLAIM=1
|
||||
export NVTE_CK_USES_BWD_V3=1
|
||||
export NCCL_IB_HCA=<your_NCCL_IB_HCA> # specify which RDMA interfaces to use for communication
|
||||
export NCCL_SOCKET_IFNAME=<your_NCCL_SOCKET_IFNAME> # your Network Interface
|
||||
export GLOO_SOCKET_IFNAME=<your_GLOO_SOCKET_IFNAME> # your Network Interface
|
||||
export NCCL_IB_GID_INDEX=3 # Set InfiniBand GID index for NCCL communication. Default is 3 for ROCE
|
||||
|
||||
.. note::
|
||||
|
||||
* Make sure correct network drivers are installed on the nodes. If inside a Docker, either install the drivers inside the Docker container or pass the network drivers from the host while creating Docker container.
|
||||
* If ``NCCL_IB_HCA`` and ``NCCL_SOCKET_IFNAME`` are not set, Primus will try to auto-detect. However, since NICs can vary accross different cluster, it is encouraged to explicitly export your NCCL parameters for the cluster.
|
||||
* To find your network interface, you can use ``ip a``.
|
||||
* To find RDMA interfaces, you can use ``ibv_devices`` to get the list of all the RDMA/IB devices.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
|
||||
|
||||
To train Llama 3.3 70B FP8 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 4 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
To train Llama 3.3 70B BF16 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 1 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 12
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
|
||||
|
||||
To train Llama 3.1 8B FP8 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
|
||||
bash ./examples/run_slurm_pretrain.sh \
|
||||
--global_batch_size 1024 \
|
||||
--fp8 hybrid
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
|
||||
|
||||
To train Llama 3.1 70B FP8 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 4 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
To train Llama 3.1 70B BF16 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 1 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 12
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
|
||||
|
||||
To train Llama 2 8B FP8 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
|
||||
NNODES=8 EXP=examples/megatron/configs/llama2_7B-pretrain.yaml bash ./examples/run_slurm_pretrain.sh --global_batch_size 2048 --fp8 hybrid
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
|
||||
|
||||
To train Llama 2 70B FP8 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 10 \
|
||||
--global_batch_size 640 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
To train Llama 2 70B BF16 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
|
||||
bash ./examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 2 \
|
||||
--global_batch_size 1536 \
|
||||
--recompute_num_layers 12
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
|
||||
|
||||
To train Mixtral 8x7B BF16 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 2 \
|
||||
--global_batch_size 256
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
|
||||
|
||||
To train Qwen2.5 72B FP8 on 8 nodes, run:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 8 \
|
||||
--global_batch_size 512 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
.. _amd-primus-megatron-lm-benchmark-test-vars-v257:
|
||||
|
||||
Key options
|
||||
-----------
|
||||
|
||||
The following are key options to take note of
|
||||
|
||||
fp8
|
||||
``hybrid`` enables FP8 GEMMs.
|
||||
|
||||
use_torch_fsdp2
|
||||
``use_torch_fsdp2: 1`` enables torch fsdp-v2. If FSDP is enabled,
|
||||
set ``use_distributed_optimizer`` and ``overlap_param_gather`` to ``false``.
|
||||
|
||||
profile
|
||||
To enable PyTorch profiling, set these parameters:
|
||||
|
||||
.. code-block:: yaml
|
||||
|
||||
profile: true
|
||||
use_pytorch_profiler: true
|
||||
profile_step_end: 7
|
||||
profile_step_start: 6
|
||||
|
||||
train_iters
|
||||
The total number of iterations (default: 50).
|
||||
|
||||
mock_data
|
||||
True by default.
|
||||
|
||||
micro_batch_size
|
||||
Micro batch size.
|
||||
|
||||
global_batch_size
|
||||
Global batch size.
|
||||
|
||||
recompute_granularity
|
||||
For activation checkpointing.
|
||||
|
||||
num_layers
|
||||
For using a reduced number of layers as with proxy models.
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
See :doc:`megatron-lm-history` to find documentation for previous releases
|
||||
of the ``ROCm/megatron-lm`` Docker image.
|
||||
@@ -2,24 +2,25 @@
|
||||
:description: How to train a model using Megatron-LM for ROCm.
|
||||
:keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
|
||||
|
||||
**********************************************
|
||||
Training a model with Primus and Megatron-Core
|
||||
**********************************************
|
||||
********************************************
|
||||
Training a model with Primus and Megatron-LM
|
||||
********************************************
|
||||
|
||||
`Primus <https://github.com/AMD-AIG-AIMA/Primus>`__ is a unified and flexible
|
||||
`Primus <https://github.com/AMD-AGI/Primus>`__ is a unified and flexible
|
||||
LLM training framework designed to streamline training. It streamlines LLM
|
||||
training on AMD Instinct accelerators using a modular, reproducible configuration paradigm.
|
||||
Primus is backend-agnostic and supports multiple training engines -- including Megatron-Core.
|
||||
Primus is backend-agnostic and supports multiple training engines -- including Megatron.
|
||||
|
||||
.. note::
|
||||
|
||||
Primus with the Megatron-Core backend is intended to replace ROCm
|
||||
Megatron-LM in this Dockerized training environment. To learn how to migrate
|
||||
workloads from Megatron-LM to Primus with Megatron-Core, see
|
||||
:doc:`previous-versions/megatron-lm-primus-migration-guide`.
|
||||
Primus with Megatron supersedes the :doc:`ROCm Megatron-LM training <megatron-lm>` workflow.
|
||||
To learn how to migrate workloads from Megatron-LM to Primus with Megatron,
|
||||
see :doc:`previous-versions/megatron-lm-primus-migration-guide`.
|
||||
|
||||
For ease of use, AMD provides a ready-to-use Docker image for MI300 series accelerators
|
||||
containing essential components for Primus and Megatron-Core.
|
||||
containing essential components for Primus and Megatron-LM. This Docker is powered by Primus
|
||||
Turbo optimizations for performance; this release adds support for Primus Turbo
|
||||
with optimized attention and grouped GEMM kernels.
|
||||
|
||||
.. note::
|
||||
|
||||
@@ -151,8 +152,8 @@ system's configuration.
|
||||
docker start primus_training_env
|
||||
docker exec -it primus_training_env bash
|
||||
|
||||
The Docker container hosts verified release tag ``v0.1.0-rc1`` of the `Primus
|
||||
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1>`__ repository.
|
||||
The Docker container hosts verified commit ``927a717`` of the `Primus
|
||||
<https://github.com/AMD-AGI/Primus/tree/927a71702784347a311ca48fd45f0f308c6ef6dd>`__ repository.
|
||||
|
||||
.. _amd-primus-megatron-lm-environment-setup:
|
||||
|
||||
@@ -160,7 +161,7 @@ Configuration
|
||||
=============
|
||||
|
||||
Primus defines a training configuration in YAML for each model in
|
||||
`examples/megatron/configs <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/megatron/configs>`__.
|
||||
`examples/megatron/configs <https://github.com/AMD-AGI/Primus/tree/927a71702784347a311ca48fd45f0f308c6ef6dd/examples/megatron/configs>`__.
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
|
||||
|
||||
@@ -205,11 +206,7 @@ You can use either mock data or real data for training.
|
||||
Tokenizer
|
||||
---------
|
||||
|
||||
In Primus, each model uses a tokenizer from Hugging Face. For example, Llama
|
||||
3.1 8B model uses ``tokenizer_model: meta-llama/Llama-3.1-8B`` and
|
||||
``tokenizer_type: Llama3Tokenizer`` defined in the `llama3.1-8B model
|
||||
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/primus/configs/models/megatron/llama3.1_8B.yaml>`__
|
||||
definition. As such, you need to set the ``HF_TOKEN`` environment variable with
|
||||
Set the ``HF_TOKEN`` environment variable with
|
||||
right permissions to access the tokenizer for each model.
|
||||
|
||||
.. code-block:: bash
|
||||
@@ -217,6 +214,14 @@ right permissions to access the tokenizer for each model.
|
||||
# Export your HF_TOKEN in the workspace
|
||||
export HF_TOKEN=<your_hftoken>
|
||||
|
||||
.. note::
|
||||
|
||||
In Primus, each model uses a tokenizer from Hugging Face. For example, Llama
|
||||
3.1 8B model uses ``tokenizer_model: meta-llama/Llama-3.1-8B`` and
|
||||
``tokenizer_type: Llama3Tokenizer`` defined in the `llama3.1-8B model
|
||||
<https://github.com/AMD-AGI/Primus/blob/927a71702784347a311ca48fd45f0f308c6ef6dd/examples/megatron/configs/llama3.1_8B-pretrain.yaml>`__
|
||||
definition.
|
||||
|
||||
.. _amd-primus-megatron-lm-run-training:
|
||||
|
||||
Run training
|
||||
@@ -237,10 +242,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
|
||||
export HSA_NO_SCRATCH_RECLAIM=1
|
||||
export NVTE_CK_USES_BWD_V3=1
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Llama 3.3 70B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run pre-training for Llama 3.3 70B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -253,6 +260,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Llama 3.1 8B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run pre-training for Llama 3.1 8B FP8, run:
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -271,6 +282,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Llama 3.1 70B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run pre-training for Llama 3.1 70B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -287,8 +302,7 @@ Once setup is complete, run the appropriate training command.
|
||||
bash ./examples/run_pretrain.sh \
|
||||
--train_iters 50 \
|
||||
--num_layers 40 \
|
||||
--fp8 hybrid \
|
||||
--no_fp8_weight_transpose_cache true
|
||||
--fp8 hybrid
|
||||
|
||||
.. note::
|
||||
|
||||
@@ -296,6 +310,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Llama 2 7B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run pre-training for Llama 2 7B FP8, run:
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -314,6 +332,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Llama 2 70B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run pre-training for Llama 2 70B BF16, run:
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -323,6 +345,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v3-proxy
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to DeepSeek-V3.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
|
||||
use the following command:
|
||||
|
||||
@@ -336,6 +362,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to DeepSeek-V2-Lite.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel),
|
||||
use the following command:
|
||||
|
||||
@@ -348,6 +378,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Mixtral 8x7B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
|
||||
use the following command:
|
||||
|
||||
@@ -358,7 +392,11 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
|
||||
|
||||
To run training on a single node for Mixtral 8x7B (MoE with expert parallel) with 4-layer proxy,
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Mixtral 8x22B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run training on a single node for Mixtral 8x22B (MoE with expert parallel) with 4-layer proxy,
|
||||
use the following command:
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -373,6 +411,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-7b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Qwen 2.5 7B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run training on a single node for Qwen 2.5 7B BF16, use the following
|
||||
command:
|
||||
|
||||
@@ -392,6 +434,10 @@ Once setup is complete, run the appropriate training command.
|
||||
|
||||
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
|
||||
|
||||
Once setup is complete, run the appropriate training command.
|
||||
The following run commands are tailored to Qwen 2.5 72B.
|
||||
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
|
||||
|
||||
To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
|
||||
|
||||
.. code-block:: shell
|
||||
@@ -403,7 +449,7 @@ Multi-node training examples
|
||||
----------------------------
|
||||
|
||||
To run training on multiple nodes, you can use the
|
||||
`run_slurm_pretrain.sh <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/run_slurm_pretrain.sh>`__
|
||||
`run_slurm_pretrain.sh <https://github.com/AMD-AGI/Primus/blob/927a71702784347a311ca48fd45f0f308c6ef6dd/examples/run_slurm_pretrain.sh>`__
|
||||
to launch the multi-node workload. Use the following steps to setup your environment:
|
||||
|
||||
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
|
||||
@@ -438,10 +484,9 @@ to launch the multi-node workload. Use the following steps to setup your environ
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 4 \
|
||||
--micro_batch_size 1 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
To train Llama 3.3 70B BF16 on 8 nodes, run:
|
||||
@@ -474,10 +519,9 @@ to launch the multi-node workload. Use the following steps to setup your environ
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 4 \
|
||||
--micro_batch_size 1 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
To train Llama 3.1 70B BF16 on 8 nodes, run:
|
||||
@@ -507,10 +551,9 @@ to launch the multi-node workload. Use the following steps to setup your environ
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 10 \
|
||||
--global_batch_size 640 \
|
||||
--micro_batch_size 2 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
To train Llama 2 70B BF16 on 8 nodes, run:
|
||||
@@ -542,10 +585,9 @@ to launch the multi-node workload. Use the following steps to setup your environ
|
||||
|
||||
NNODES=8 EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
|
||||
bash examples/run_slurm_pretrain.sh \
|
||||
--micro_batch_size 8 \
|
||||
--global_batch_size 512 \
|
||||
--micro_batch_size 4 \
|
||||
--global_batch_size 256 \
|
||||
--recompute_num_layers 80 \
|
||||
--no_fp8_weight_transpose_cache true \
|
||||
--fp8 hybrid
|
||||
|
||||
.. _amd-primus-megatron-lm-benchmark-test-vars:
|
||||
@@ -590,6 +632,18 @@ recompute_granularity
|
||||
num_layers
|
||||
For using a reduced number of layers as with proxy models.
|
||||
|
||||
Further reading
|
||||
===============
|
||||
|
||||
- For an introduction to Primus, see `Primus: A Lightweight, Unified Training
|
||||
Framework for Large Models on AMD GPUs <https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html>`__.
|
||||
|
||||
- To learn more about system settings and management practices to configure your system for
|
||||
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
|
||||
|
||||
- For a list of other ready-made Docker images for AI with ROCm, see
|
||||
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
|
||||
|
||||
Previous versions
|
||||
=================
|
||||
|
||||
@@ -598,5 +652,4 @@ of the ``ROCm/megatron-lm`` Docker image.
|
||||
|
||||
This training environment now uses Primus with Megatron as the primary
|
||||
configuration. Limited support for the legacy ROCm Megatron-LM is still
|
||||
available. For instructions on using ROCm Megatron-LM, see the
|
||||
:doc:`megatron-lm` document.
|
||||
available; see the :doc:`megatron-lm` documentation.
|
||||
|
||||
Reference in New Issue
Block a user